DCU Python Tutorials

An interactive introduction to Python that runs in your browser

Author

Damien Dupré, DCU Business School

Each grey box on this page is a real Python console. You can type in it, change the code, and run it. The Python interpreter (a build called Pyodide) has been compiled to run inside your web browser, so there is nothing to install, no account to create, and no server to connect to.

A few things worth knowing before you start:

Edit the code in a box, then press Run Code to see what it produces.
Where you see ______ in the code, replace it with the missing piece. The comments next to it tell you what is expected.
If you get stuck, use the Show Hints or Show Solution buttons under each exercise.

The first time you run a box, Python needs a few seconds to start up and download the packages it uses (pandas and plotnine). That only happens once, so please be patient.

Getting started

Let us check that everything is working. Just press Run Code on the box below.

If Hello World appeared underneath, then Python is running in your browser and you are ready to go.

Chapter 1: Python basics

This chapter covers the pieces you will reach for again and again: variables, lists, and dictionaries. You do not need any extra package here, since all of this is plain Python.

Variables and f-strings

A variable stores a value with = so that you can use it again later. An f-string, which is text written as f"...", lets you drop a variable straight into the text by putting its name in curly braces. In each exercise below, replace every ______ using the values described in the comments.

my_age  = 25
my_name = "Damien"

print(f"My name is {my_name} and I am {my_age} years old")

my_numbers = [1, 2, 3, 4, 5]
print("Length:", len(my_numbers))

Lists and dictionaries

A list is an ordered collection written in square brackets [ ], and you reach into it by position. Python counts from 0, so the second item is [1], not [2]. A dictionary stores values under named keys, written in curly braces { }.

provinces = ["Connacht", "Leinster", "Munster", "Ulster"]

print(provinces[1])

print("Number of provinces:", len(provinces))

numbers = [10, 20, 30, 40, 50]
print("Sum:", sum(numbers))

student = {"name": "Alice", "age": 21, "course": "Business Analytics"}
print(student["name"], "is", student["age"], "years old")

Chapter 2: Data wrangling with pandas

Now for some real data. From here on we will use the gapminder dataset, which records life expectancy, population, and GDP per capita for most countries between 1952 and 2007. We read it straight from the web with pandas, the standard Python library for working with tables (which pandas calls DataFrames).

A quick note on Polars and pandas. The classroom version of this course uses Polars. Polars is written in Rust and has no WebAssembly build, so it cannot run inside the browser. For this in-browser edition we use pandas instead, which Pyodide includes out of the box. The ideas are the same in both: filter rows, pick columns, and group.

Run the box below to load the data and take a first look.

The columns are country, continent, year, lifeExp, pop, and gdpPercap.

Filter rows and select columns

You filter rows with a condition inside square brackets, and you select columns by passing a list of their names.

gapminder[(gapminder["year"] == 2007) & (gapminder["pop"] > 100000000)]

gapminder[["country", "lifeExp"]]

Group and summarise

groupby() splits the table into groups. You then pick a column and apply a summary such as .mean(), which answers questions like “what is the average life expectancy on each continent?”.

(
    gapminder[gapminder["year"] == 2007]
    .groupby("continent")["lifeExp"]
    .mean()
)

Chapter 3: Visualisation with plotnine

plotnine brings the grammar of graphics to Python, which is the same approach as ggplot2 in R. You start from the data, map variables to things you can see (the x position, the y position, the colour, and so on) with aes(), and then add a geometry such as geom_point() to draw them.

from plotnine import ggplot, aes, geom_point, scale_x_log10, labs, theme_minimal

df = gapminder[gapminder["year"] == 2007]

(
    ggplot(df, aes(x="gdpPercap", y="lifeExp", color="continent"))
    + geom_point(size=2, alpha=0.7)
    + scale_x_log10()
    + labs(
        title="Wealth and health across the world, 2007",
        x="GDP per capita (log scale)",
        y="Life expectancy (years)",
        color="Continent",
    )
    + theme_minimal()
)

Where to go next

You have just filtered, summarised, and plotted real data without installing anything on your machine. There is of course a lot more to Python than this, and the only way to get comfortable with it is to keep writing and running code.

If you would like the longer version of this course, the original one (which runs on a server) is at dcu-python-tutorials. If you prefer R, the same ideas are in the R live tutorials.

One last thing: every box on this page keeps your edits for as long as the tab is open, so do feel free to experiment. If you want the original code back, press Start Over on the exercise.